Researchers developed a multi-phase machine-learning framework to rapidly predict electronic band gaps and screen oxide perovskites for potential photovoltaic applications. Starting from more than half a million candidate structures, the approach progressively narrowed the search to a small group of band-gap-compatible compositions that now require higher-level computational and experimental validation.
